Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations180282
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Categorical5
DateTime2

Reproduction

Analysis started2025-02-15 11:28:53.079957
Analysis finished2025-02-15 11:29:08.184580
Duration15.1 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID_Equipo
Real number (ℝ)

Distinct499
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.76427
Minimum1
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:08.283774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q1119
median246
Q3370
95-th percentile474
Maximum499
Range498
Interquartile range (IQR)251

Descriptive statistics

Standard deviation144.34393
Coefficient of variation (CV)0.58732674
Kurtosis-1.211411
Mean245.76427
Median Absolute Deviation (MAD)126
Skewness0.0301803
Sum44306874
Variance20835.169
MonotonicityIncreasing
2025-02-15T12:29:08.417059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172 792
 
0.4%
32 780
 
0.4%
51 729
 
0.4%
356 725
 
0.4%
283 713
 
0.4%
159 702
 
0.4%
397 696
 
0.4%
8 675
 
0.4%
79 648
 
0.4%
312 644
 
0.4%
Other values (489) 173178
96.1%
ValueCountFrequency (%)
1 320
0.2%
2 169
 
0.1%
3 288
0.2%
4 400
0.2%
5 493
0.3%
6 552
0.3%
7 165
 
0.1%
8 675
0.4%
9 208
 
0.1%
10 432
0.2%
ValueCountFrequency (%)
499 160
 
0.1%
498 336
0.2%
497 418
0.2%
496 504
0.3%
495 437
0.2%
494 345
0.2%
493 378
0.2%
492 391
0.2%
491 209
0.1%
490 154
 
0.1%

Tipo_Equipo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Generador
50680 
Transformador
44334 
Compresor
43147 
Motor
42121 

Length

Max length13
Median length9
Mean length9.0491009
Min length5

Characters and Unicode

Total characters1631390
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotor
2nd rowMotor
3rd rowMotor
4th rowMotor
5th rowMotor

Common Values

ValueCountFrequency (%)
Generador 50680
28.1%
Transformador 44334
24.6%
Compresor 43147
23.9%
Motor 42121
23.4%

Length

2025-02-15T12:29:08.532489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T12:29:08.617284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
generador 50680
28.1%
transformador 44334
24.6%
compresor 43147
23.9%
motor 42121
23.4%

Most occurring characters

ValueCountFrequency (%)
r 362777
22.2%
o 309884
19.0%
e 144507
 
8.9%
a 139348
 
8.5%
n 95014
 
5.8%
d 95014
 
5.8%
s 87481
 
5.4%
m 87481
 
5.4%
G 50680
 
3.1%
T 44334
 
2.7%
Other values (5) 214870
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1631390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 362777
22.2%
o 309884
19.0%
e 144507
 
8.9%
a 139348
 
8.5%
n 95014
 
5.8%
d 95014
 
5.8%
s 87481
 
5.4%
m 87481
 
5.4%
G 50680
 
3.1%
T 44334
 
2.7%
Other values (5) 214870
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1631390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 362777
22.2%
o 309884
19.0%
e 144507
 
8.9%
a 139348
 
8.5%
n 95014
 
5.8%
d 95014
 
5.8%
s 87481
 
5.4%
m 87481
 
5.4%
G 50680
 
3.1%
T 44334
 
2.7%
Other values (5) 214870
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1631390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 362777
22.2%
o 309884
19.0%
e 144507
 
8.9%
a 139348
 
8.5%
n 95014
 
5.8%
d 95014
 
5.8%
s 87481
 
5.4%
m 87481
 
5.4%
G 50680
 
3.1%
T 44334
 
2.7%
Other values (5) 214870
13.2%

Fabricante
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
ABB
46782 
GE
46405 
Siemens
46131 
Schneider
40964 

Length

Max length9
Median length7
Mean length5.1294583
Min length2

Characters and Unicode

Total characters924749
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSchneider
2nd rowSchneider
3rd rowSchneider
4th rowSchneider
5th rowSchneider

Common Values

ValueCountFrequency (%)
ABB 46782
25.9%
GE 46405
25.7%
Siemens 46131
25.6%
Schneider 40964
22.7%

Length

2025-02-15T12:29:08.733768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T12:29:08.817174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
abb 46782
25.9%
ge 46405
25.7%
siemens 46131
25.6%
schneider 40964
22.7%

Most occurring characters

ValueCountFrequency (%)
e 174190
18.8%
B 93564
10.1%
S 87095
9.4%
i 87095
9.4%
n 87095
9.4%
A 46782
 
5.1%
G 46405
 
5.0%
E 46405
 
5.0%
m 46131
 
5.0%
s 46131
 
5.0%
Other values (4) 163856
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 924749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 174190
18.8%
B 93564
10.1%
S 87095
9.4%
i 87095
9.4%
n 87095
9.4%
A 46782
 
5.1%
G 46405
 
5.0%
E 46405
 
5.0%
m 46131
 
5.0%
s 46131
 
5.0%
Other values (4) 163856
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 924749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 174190
18.8%
B 93564
10.1%
S 87095
9.4%
i 87095
9.4%
n 87095
9.4%
A 46782
 
5.1%
G 46405
 
5.0%
E 46405
 
5.0%
m 46131
 
5.0%
s 46131
 
5.0%
Other values (4) 163856
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 924749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 174190
18.8%
B 93564
10.1%
S 87095
9.4%
i 87095
9.4%
n 87095
9.4%
A 46782
 
5.1%
G 46405
 
5.0%
E 46405
 
5.0%
m 46131
 
5.0%
s 46131
 
5.0%
Other values (4) 163856
17.7%

Modelo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Z300
52377 
Y200
46567 
X100
42041 
M400
39297 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters721128
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX100
2nd rowX100
3rd rowX100
4th rowX100
5th rowX100

Common Values

ValueCountFrequency (%)
Z300 52377
29.1%
Y200 46567
25.8%
X100 42041
23.3%
M400 39297
21.8%

Length

2025-02-15T12:29:08.916744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T12:29:09.000249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
z300 52377
29.1%
y200 46567
25.8%
x100 42041
23.3%
m400 39297
21.8%

Most occurring characters

ValueCountFrequency (%)
0 360564
50.0%
Z 52377
 
7.3%
3 52377
 
7.3%
Y 46567
 
6.5%
2 46567
 
6.5%
X 42041
 
5.8%
1 42041
 
5.8%
M 39297
 
5.4%
4 39297
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 721128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 360564
50.0%
Z 52377
 
7.3%
3 52377
 
7.3%
Y 46567
 
6.5%
2 46567
 
6.5%
X 42041
 
5.8%
1 42041
 
5.8%
M 39297
 
5.4%
4 39297
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 721128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 360564
50.0%
Z 52377
 
7.3%
3 52377
 
7.3%
Y 46567
 
6.5%
2 46567
 
6.5%
X 42041
 
5.8%
1 42041
 
5.8%
M 39297
 
5.4%
4 39297
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 721128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 360564
50.0%
Z 52377
 
7.3%
3 52377
 
7.3%
Y 46567
 
6.5%
2 46567
 
6.5%
X 42041
 
5.8%
1 42041
 
5.8%
M 39297
 
5.4%
4 39297
 
5.4%

Potencia_kW
Real number (ℝ)

Distinct480
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2460.0207
Minimum53
Maximum4997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:09.116549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile278
Q11306
median2431
Q33562
95-th percentile4764
Maximum4997
Range4944
Interquartile range (IQR)2256

Descriptive statistics

Standard deviation1387.2939
Coefficient of variation (CV)0.56393587
Kurtosis-1.0750278
Mean2460.0207
Median Absolute Deviation (MAD)1127
Skewness0.064099582
Sum4.4349745 × 108
Variance1924584.4
MonotonicityNot monotonic
2025-02-15T12:29:09.566501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1805 1071
 
0.6%
3029 1037
 
0.6%
250 966
 
0.5%
562 920
 
0.5%
3212 850
 
0.5%
404 804
 
0.4%
4080 793
 
0.4%
4194 792
 
0.4%
876 780
 
0.4%
1536 730
 
0.4%
Other values (470) 171539
95.2%
ValueCountFrequency (%)
53 418
0.2%
79 231
0.1%
137 360
0.2%
146 460
0.3%
153 288
0.2%
163 435
0.2%
173 378
0.2%
187 345
0.2%
197 462
0.3%
210 380
0.2%
ValueCountFrequency (%)
4997 260
0.1%
4993 414
0.2%
4992 323
0.2%
4974 380
0.2%
4948 454
0.3%
4947 462
0.3%
4945 276
0.2%
4930 325
0.2%
4929 266
0.1%
4914 156
 
0.1%
Distinct489
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5177.9289
Minimum525
Maximum9993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:09.666886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile833
Q12867
median5200
Q37392
95-th percentile9450
Maximum9993
Range9468
Interquartile range (IQR)4525

Descriptive statistics

Standard deviation2720.0124
Coefficient of variation (CV)0.52530895
Kurtosis-1.1454298
Mean5177.9289
Median Absolute Deviation (MAD)2282
Skewness-0.013499667
Sum9.3348737 × 108
Variance7398467.4
MonotonicityNot monotonic
2025-02-15T12:29:09.761214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2270 924
 
0.5%
6494 792
 
0.4%
8101 792
 
0.4%
9450 780
 
0.4%
1534 776
 
0.4%
9026 738
 
0.4%
6528 729
 
0.4%
6101 725
 
0.4%
697 713
 
0.4%
2386 711
 
0.4%
Other values (479) 172602
95.7%
ValueCountFrequency (%)
525 204
 
0.1%
549 416
0.2%
564 368
0.2%
572 342
0.2%
585 240
 
0.1%
618 342
0.2%
654 255
 
0.1%
655 645
0.4%
661 342
0.2%
663 208
 
0.1%
ValueCountFrequency (%)
9993 224
0.1%
9933 432
0.2%
9926 266
0.1%
9921 288
0.2%
9916 342
0.2%
9909 260
0.1%
9881 378
0.2%
9851 260
0.1%
9836 320
0.2%
9829 360
0.2%

ID_Orden
Real number (ℝ)

Distinct10000
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5008.7625
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:09.850172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile501
Q12512
median5010
Q37500
95-th percentile9503.95
Maximum10000
Range9999
Interquartile range (IQR)4988

Descriptive statistics

Standard deviation2886.3028
Coefficient of variation (CV)0.57625069
Kurtosis-1.1984987
Mean5008.7625
Median Absolute Deviation (MAD)2494
Skewness-0.0027734482
Sum9.0298972 × 108
Variance8330743.9
MonotonicityNot monotonic
2025-02-15T12:29:09.950774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5505 34
 
< 0.1%
6591 34
 
< 0.1%
7869 34
 
< 0.1%
4373 34
 
< 0.1%
4082 34
 
< 0.1%
3324 34
 
< 0.1%
2622 34
 
< 0.1%
1567 34
 
< 0.1%
812 34
 
< 0.1%
692 34
 
< 0.1%
Other values (9990) 179942
99.8%
ValueCountFrequency (%)
1 17
< 0.1%
2 13
< 0.1%
3 13
< 0.1%
4 18
< 0.1%
5 24
< 0.1%
6 20
< 0.1%
7 18
< 0.1%
8 15
< 0.1%
9 26
< 0.1%
10 14
< 0.1%
ValueCountFrequency (%)
10000 19
< 0.1%
9999 14
< 0.1%
9998 23
< 0.1%
9997 18
< 0.1%
9996 17
< 0.1%
9995 12
< 0.1%
9994 17
< 0.1%
9993 17
< 0.1%
9992 23
< 0.1%
9991 26
< 0.1%
Distinct10000
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2020-01-01 00:00:00
Maximum2021-02-20 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-15T12:29:10.054413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:10.161562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Correctivo
91643 
Preventivo
88639 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1802820
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorrectivo
2nd rowCorrectivo
3rd rowCorrectivo
4th rowCorrectivo
5th rowCorrectivo

Common Values

ValueCountFrequency (%)
Correctivo 91643
50.8%
Preventivo 88639
49.2%

Length

2025-02-15T12:29:10.267552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T12:29:10.350463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
correctivo 91643
50.8%
preventivo 88639
49.2%

Most occurring characters

ValueCountFrequency (%)
o 271925
15.1%
r 271925
15.1%
e 268921
14.9%
v 268921
14.9%
t 180282
10.0%
i 180282
10.0%
C 91643
 
5.1%
c 91643
 
5.1%
P 88639
 
4.9%
n 88639
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1802820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 271925
15.1%
r 271925
15.1%
e 268921
14.9%
v 268921
14.9%
t 180282
10.0%
i 180282
10.0%
C 91643
 
5.1%
c 91643
 
5.1%
P 88639
 
4.9%
n 88639
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1802820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 271925
15.1%
r 271925
15.1%
e 268921
14.9%
v 268921
14.9%
t 180282
10.0%
i 180282
10.0%
C 91643
 
5.1%
c 91643
 
5.1%
P 88639
 
4.9%
n 88639
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1802820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 271925
15.1%
r 271925
15.1%
e 268921
14.9%
v 268921
14.9%
t 180282
10.0%
i 180282
10.0%
C 91643
 
5.1%
c 91643
 
5.1%
P 88639
 
4.9%
n 88639
 
4.9%

Costo_Mantenimiento
Real number (ℝ)

Distinct9889
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4987.9233
Minimum100.72
Maximum9999.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:10.434589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100.72
5-th percentile599.66
Q12521.34
median4958.96
Q37430.59
95-th percentile9463.75
Maximum9999.61
Range9898.89
Interquartile range (IQR)4909.25

Descriptive statistics

Standard deviation2830.5765
Coefficient of variation (CV)0.56748596
Kurtosis-1.1899117
Mean4987.9233
Median Absolute Deviation (MAD)2457.89
Skewness0.025402272
Sum8.9923279 × 108
Variance8012163.1
MonotonicityNot monotonic
2025-02-15T12:29:10.549877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4978.504601 558
 
0.3%
740.09 52
 
< 0.1%
3412.96 50
 
< 0.1%
454.97 50
 
< 0.1%
4253.19 49
 
< 0.1%
7431.71 46
 
< 0.1%
6702.01 45
 
< 0.1%
2020.21 44
 
< 0.1%
8632.19 44
 
< 0.1%
8119.42 44
 
< 0.1%
Other values (9879) 179300
99.5%
ValueCountFrequency (%)
100.72 18
< 0.1%
100.84 25
< 0.1%
101.4 15
< 0.1%
103.74 15
< 0.1%
105.12 19
< 0.1%
107.06 21
< 0.1%
107.98 19
< 0.1%
108.34 19
< 0.1%
110.21 18
< 0.1%
110.37 20
< 0.1%
ValueCountFrequency (%)
9999.61 19
< 0.1%
9997.24 18
< 0.1%
9996.53 22
< 0.1%
9996.01 15
< 0.1%
9995.88 25
< 0.1%
9995.69 12
< 0.1%
9994.98 15
< 0.1%
9994.63 22
< 0.1%
9993.96 12
< 0.1%
9992.95 15
< 0.1%

Duracion_Horas
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.079109
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:10.649535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median24
Q336
95-th percentile45
Maximum47
Range46
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.509449
Coefficient of variation (CV)0.56104438
Kurtosis-1.1910113
Mean24.079109
Median Absolute Deviation (MAD)12
Skewness-0.015219981
Sum4341030
Variance182.50521
MonotonicityNot monotonic
2025-02-15T12:29:10.750783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
36 4497
 
2.5%
24 4310
 
2.4%
8 4306
 
2.4%
32 4248
 
2.4%
37 4233
 
2.3%
5 4228
 
2.3%
28 4150
 
2.3%
31 4137
 
2.3%
25 4119
 
2.3%
6 4094
 
2.3%
Other values (37) 137960
76.5%
ValueCountFrequency (%)
1 3759
2.1%
2 3397
1.9%
3 3731
2.1%
4 3764
2.1%
5 4228
2.3%
6 4094
2.3%
7 3853
2.1%
8 4306
2.4%
9 3676
2.0%
10 3785
2.1%
ValueCountFrequency (%)
47 4025
2.2%
46 4056
2.2%
45 3554
2.0%
44 3386
1.9%
43 4052
2.2%
42 3176
1.8%
41 3858
2.1%
40 3711
2.1%
39 3935
2.2%
38 3821
2.1%

Ubicacion
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Planta Este
47112 
Planta Norte
45370 
Planta Sur
44700 
Planta Oeste
43100 

Length

Max length12
Median length11
Mean length11.242786
Min length10

Characters and Unicode

Total characters2026872
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlanta Este
2nd rowPlanta Este
3rd rowPlanta Este
4th rowPlanta Este
5th rowPlanta Este

Common Values

ValueCountFrequency (%)
Planta Este 47112
26.1%
Planta Norte 45370
25.2%
Planta Sur 44700
24.8%
Planta Oeste 43100
23.9%

Length

2025-02-15T12:29:10.865277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T12:29:10.950358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
planta 180282
50.0%
este 47112
 
13.1%
norte 45370
 
12.6%
sur 44700
 
12.4%
oeste 43100
 
12.0%

Most occurring characters

ValueCountFrequency (%)
a 360564
17.8%
t 315864
15.6%
P 180282
8.9%
l 180282
8.9%
n 180282
8.9%
180282
8.9%
e 178682
8.8%
s 90212
 
4.5%
r 90070
 
4.4%
E 47112
 
2.3%
Other values (5) 223240
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2026872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 360564
17.8%
t 315864
15.6%
P 180282
8.9%
l 180282
8.9%
n 180282
8.9%
180282
8.9%
e 178682
8.8%
s 90212
 
4.5%
r 90070
 
4.4%
E 47112
 
2.3%
Other values (5) 223240
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2026872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 360564
17.8%
t 315864
15.6%
P 180282
8.9%
l 180282
8.9%
n 180282
8.9%
180282
8.9%
e 178682
8.8%
s 90212
 
4.5%
r 90070
 
4.4%
E 47112
 
2.3%
Other values (5) 223240
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2026872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 360564
17.8%
t 315864
15.6%
P 180282
8.9%
l 180282
8.9%
n 180282
8.9%
180282
8.9%
e 178682
8.8%
s 90212
 
4.5%
r 90070
 
4.4%
E 47112
 
2.3%
Other values (5) 223240
11.0%

ID_Registro
Real number (ℝ)

Distinct9000
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4496.0054
Minimum1
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:11.067109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile448
Q12246
median4492
Q36748
95-th percentile8547
Maximum9000
Range8999
Interquartile range (IQR)4502

Descriptive statistics

Standard deviation2596.925
Coefficient of variation (CV)0.57760719
Kurtosis-1.2005878
Mean4496.0054
Median Absolute Deviation (MAD)2251
Skewness0.0032024181
Sum8.1054884 × 108
Variance6744019.5
MonotonicityNot monotonic
2025-02-15T12:29:11.167247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1968 36
 
< 0.1%
6392 36
 
< 0.1%
2437 36
 
< 0.1%
2695 36
 
< 0.1%
3172 36
 
< 0.1%
256 36
 
< 0.1%
554 36
 
< 0.1%
588 36
 
< 0.1%
1612 36
 
< 0.1%
4330 36
 
< 0.1%
Other values (8990) 179922
99.8%
ValueCountFrequency (%)
1 21
< 0.1%
2 21
< 0.1%
3 15
< 0.1%
4 24
< 0.1%
5 19
< 0.1%
6 18
< 0.1%
7 18
< 0.1%
8 9
 
< 0.1%
9 21
< 0.1%
10 24
< 0.1%
ValueCountFrequency (%)
9000 22
< 0.1%
8999 23
< 0.1%
8998 22
< 0.1%
8997 15
< 0.1%
8996 23
< 0.1%
8995 26
< 0.1%
8994 20
< 0.1%
8993 19
< 0.1%
8992 11
< 0.1%
8991 26
< 0.1%
Distinct9000
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2020-01-01 00:00:00
Maximum2021-01-09 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-15T12:29:11.283288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:11.383529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Temperatura_C
Real number (ℝ)

Distinct5909
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.732548
Minimum50.01
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:11.500055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50.01
5-th percentile54.89
Q174.59
median99.89
Q3124.61
95-th percentile144.98
Maximum150
Range99.99
Interquartile range (IQR)50.02

Descriptive statistics

Standard deviation28.816844
Coefficient of variation (CV)0.28894122
Kurtosis-1.1911086
Mean99.732548
Median Absolute Deviation (MAD)25
Skewness0.0049794407
Sum17979983
Variance830.41048
MonotonicityNot monotonic
2025-02-15T12:29:11.600319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.7100889 599
 
0.3%
104.24 148
 
0.1%
96 147
 
0.1%
87.71 134
 
0.1%
110.32 126
 
0.1%
64.88 121
 
0.1%
69.87 115
 
0.1%
51.06 115
 
0.1%
83.46 115
 
0.1%
94.82 113
 
0.1%
Other values (5899) 178549
99.0%
ValueCountFrequency (%)
50.01 23
 
< 0.1%
50.02 93
0.1%
50.03 39
< 0.1%
50.04 19
 
< 0.1%
50.05 24
 
< 0.1%
50.06 23
 
< 0.1%
50.07 22
 
< 0.1%
50.09 47
< 0.1%
50.11 23
 
< 0.1%
50.12 40
< 0.1%
ValueCountFrequency (%)
150 18
< 0.1%
149.99 38
< 0.1%
149.95 19
< 0.1%
149.94 14
 
< 0.1%
149.93 18
< 0.1%
149.91 19
< 0.1%
149.9 21
< 0.1%
149.89 17
< 0.1%
149.88 25
< 0.1%
149.86 13
 
< 0.1%

Vibracion_mm_s
Real number (ℝ)

Distinct491
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5415371
Minimum0.1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:11.716046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.34
Q11.33
median2.53
Q33.76
95-th percentile4.77
Maximum5
Range4.9
Interquartile range (IQR)2.43

Descriptive statistics

Standard deviation1.4103468
Coefficient of variation (CV)0.55491883
Kurtosis-1.1813304
Mean2.5415371
Median Absolute Deviation (MAD)1.22
Skewness0.0061235069
Sum458193.39
Variance1.9890781
MonotonicityNot monotonic
2025-02-15T12:29:11.817140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.95 745
 
0.4%
3.89 654
 
0.4%
1.98 646
 
0.4%
0.72 639
 
0.4%
4.97 608
 
0.3%
1.11 590
 
0.3%
3.53 586
 
0.3%
4.04 583
 
0.3%
2.03 580
 
0.3%
2.47 572
 
0.3%
Other values (481) 174079
96.6%
ValueCountFrequency (%)
0.1 158
 
0.1%
0.11 448
0.2%
0.12 289
0.2%
0.13 411
0.2%
0.14 344
0.2%
0.15 399
0.2%
0.16 422
0.2%
0.17 547
0.3%
0.18 199
 
0.1%
0.19 430
0.2%
ValueCountFrequency (%)
5 238
 
0.1%
4.99 317
0.2%
4.98 319
0.2%
4.97 608
0.3%
4.96 236
 
0.1%
4.95 423
0.2%
4.94 391
0.2%
4.93 441
0.2%
4.92 320
0.2%
4.91 477
0.3%

Horas_Operativas
Real number (ℝ)

Distinct8548
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50383.226
Minimum0
Maximum99982
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-02-15T12:29:11.917178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5056
Q125243
median50551
Q375581
95-th percentile94904.8
Maximum99982
Range99982
Interquartile range (IQR)50338

Descriptive statistics

Standard deviation28911.148
Coefficient of variation (CV)0.57382488
Kurtosis-1.202873
Mean50383.226
Median Absolute Deviation (MAD)25159
Skewness-0.022789911
Sum9.0831887 × 109
Variance8.3585451 × 108
MonotonicityNot monotonic
2025-02-15T12:29:12.016600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50427.32768 819
 
0.5%
77892 69
 
< 0.1%
3651 65
 
< 0.1%
1190 63
 
< 0.1%
10919 60
 
< 0.1%
14779 60
 
< 0.1%
39175 58
 
< 0.1%
44590 58
 
< 0.1%
56546 57
 
< 0.1%
33991 57
 
< 0.1%
Other values (8538) 178916
99.2%
ValueCountFrequency (%)
0 21
< 0.1%
18 32
< 0.1%
24 19
< 0.1%
44 15
 
< 0.1%
49 14
 
< 0.1%
87 26
< 0.1%
90 24
< 0.1%
109 20
< 0.1%
111 45
< 0.1%
125 27
< 0.1%
ValueCountFrequency (%)
99982 21
< 0.1%
99939 19
 
< 0.1%
99935 52
< 0.1%
99933 20
 
< 0.1%
99905 19
 
< 0.1%
99898 17
 
< 0.1%
99890 23
< 0.1%
99881 15
 
< 0.1%
99874 22
< 0.1%
99872 20
 
< 0.1%

Interactions

2025-02-15T12:29:06.583494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:57.799967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.716834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.650545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.816990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.733217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.635149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.900457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.800461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.683863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.683405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:57.883328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.817506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.750950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.917541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.817161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.733602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.983824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.883589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.783968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.777489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:57.983756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.901682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.967213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.000630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.917305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.816975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.083878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.967079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.867540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.867365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.067160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.999812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.167341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.100518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.000151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.917600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.166964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.066834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.953828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.965665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.150238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.083295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.250135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.183558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.083742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.383408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.250722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.151816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.053995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:07.056105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.250311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.167224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.346050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.284196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.183577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.467453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.350154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.250500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.133398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:07.149905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.333838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.267227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.433447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.366601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.267276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.550299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.433072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.333489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.216820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:07.233392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.439898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.350101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.531827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.449930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.350294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.634588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.517402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.417159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.316755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:07.316957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.533362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.450193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.633640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.550038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.450327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.717224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.617011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.500439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.400061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:07.416587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:58.634187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:28:59.550714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:00.717040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:01.633851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:02.533620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:03.800095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:04.699528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:05.600087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T12:29:06.484250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-15T12:29:12.100519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Costo_MantenimientoDuracion_HorasFabricanteHoras_OperativasHoras_Recomendadas_RevisionID_EquipoID_OrdenID_RegistroModeloPotencia_kWTemperatura_CTipo_EquipoTipo_MantenimientoUbicacionVibracion_mm_s
Costo_Mantenimiento1.0000.0030.0390.002-0.0030.0050.0150.0030.0250.012-0.0000.0360.0280.031-0.001
Duracion_Horas0.0031.0000.035-0.0010.003-0.012-0.0030.0040.033-0.005-0.0020.0250.0320.026-0.002
Fabricante0.0390.0351.0000.0350.1590.1560.0300.0330.0840.1560.0320.1050.0270.0180.034
Horas_Operativas0.002-0.0010.0351.000-0.0110.0090.002-0.0070.0320.009-0.0120.0260.0000.0000.002
Horas_Recomendadas_Revision-0.0030.0030.159-0.0111.0000.005-0.0070.0180.1300.074-0.0150.1330.0330.028-0.005
ID_Equipo0.005-0.0120.1560.0090.0051.000-0.013-0.0010.148-0.0120.0110.1370.0210.035-0.001
ID_Orden0.015-0.0030.0300.002-0.007-0.0131.000-0.0010.028-0.0070.0010.0320.0260.0310.003
ID_Registro0.0030.0040.033-0.0070.018-0.001-0.0011.0000.0320.0210.0100.0240.0030.002-0.005
Modelo0.0250.0330.0840.0320.1300.1480.0280.0321.0000.1250.0240.0460.0200.0200.034
Potencia_kW0.012-0.0050.1560.0090.074-0.012-0.0070.0210.1251.000-0.0030.1340.0430.0310.019
Temperatura_C-0.000-0.0020.032-0.012-0.0150.0110.0010.0100.024-0.0031.0000.0260.0050.000-0.004
Tipo_Equipo0.0360.0250.1050.0260.1330.1370.0320.0240.0460.1340.0261.0000.0240.0110.028
Tipo_Mantenimiento0.0280.0320.0270.0000.0330.0210.0260.0030.0200.0430.0050.0241.0000.0080.000
Ubicacion0.0310.0260.0180.0000.0280.0350.0310.0020.0200.0310.0000.0110.0081.0000.000
Vibracion_mm_s-0.001-0.0020.0340.002-0.005-0.0010.003-0.0050.0340.019-0.0040.0280.0000.0001.000

Missing values

2025-02-15T12:29:07.557238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-15T12:29:07.864349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionID_OrdenFecha_OrdenesTipo_MantenimientoCosto_MantenimientoDuracion_HorasUbicacionID_RegistroFecha_RegistrosTemperatura_CVibracion_mm_sHoras_Operativas
01MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este715.02020-01-30 18:00:00144.892.6735298.0
11MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este1094.02020-02-15 13:00:00104.970.5163478.0
21MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este1811.02020-03-16 10:00:00115.030.7229082.0
31MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este1897.02020-03-20 00:00:00119.493.1791842.0
41MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este2241.02020-04-03 08:00:00110.471.7654889.0
51MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este2783.02020-04-25 22:00:0096.534.5162421.0
61MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este3492.02020-05-25 11:00:00115.574.218167.0
71MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este3973.02020-06-14 12:00:00102.003.3922728.0
81MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este4056.02020-06-17 23:00:0095.930.7657056.0
91MotorSchneiderX1001009.09656929.02020-02-08 16:00:00Correctivo3143.588.0Planta Este4552.02020-07-08 15:00:0062.732.6458627.0
ID_EquipoTipo_EquipoFabricanteModeloPotencia_kWHoras_Recomendadas_RevisionID_OrdenFecha_OrdenesTipo_MantenimientoCosto_MantenimientoDuracion_HorasUbicacionID_RegistroFecha_RegistrosTemperatura_CVibracion_mm_sHoras_Operativas
180272499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste1234.02020-02-21 09:00:0080.583.8595712.0
180273499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste1322.02020-02-25 01:00:00113.493.2276656.0
180274499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste2391.02020-04-09 14:00:0083.964.1435475.0
180275499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste2729.02020-04-23 16:00:00144.490.6398945.0
180276499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste3279.02020-05-16 14:00:0074.683.5545486.0
180277499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste3387.02020-05-21 02:00:0069.172.7668688.0
180278499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste3756.02020-06-05 11:00:00129.460.771400.0
180279499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste5345.02020-08-10 16:00:0069.392.8652083.0
180280499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste6203.02020-09-15 10:00:00130.880.8553659.0
180281499MotorABBM400877.069079332.02021-01-23 19:00:00Preventivo7158.6140.0Planta Oeste6557.02020-09-30 04:00:0058.004.9882396.0